An Order of Magnitude Signal-to-Noise Improvement of Magnetic Resonance Spectra using a Segmented-Overlap Fourier-Filtering and Averaging (SOFFA) Approach.

Abstract

Segmented-Overlap Fourier-Filtering and Averaging (SOFFA) data acquisition method is described in detail for magnetic resonance spectroscopy. In this work the four processes that encompass the SOFFA data acquisition method are detailed: (i) oversampling spectral segments, (ii) Fourier block-filtering, (iii) segment-overlap averaging, and (iv) decimation. Three experimental examples are shown. Conventional Continuous Wave (CW) Electron Paramagnetic Resonance (EPR) is compared to SOFFA-CW of a single reduced [4Fe-4S]+ (S=1/2) at concentrations of 1 mM, 100 {micro}M, and 10 {micro}M showing an average increase in concentration sensitivity by a factor of 5.6. Experimental comparison of CW and SOFFA nonadiabatic rapid scan (SOFFA-NARS) data with similar filter parameters and field-modulation amplitude demonstrates a factor of 10.3 in signal-to-noise improvement for a 150 {micro}M sitedirected spin-labeled Hemoglobin in 82% glycerol at 18C. Conventional and SOFFA rapid scan (SOFFA-RS) data was collected for a minuscule amount of lithium phthalocyanine (LiPC) and a factor of 9.6 in signal-to-noise improvement is demonstrated. The signal-tonoise improvements were made for the same data acquisition times on standard commercial instruments. This method can be implemented to perform real-time segmented processing and, combined with more sophisticated averaging methods, will push the state-of-the-art sensitivity in magnetic resonance spectroscopy.